Abstract
Defect detection in composite materials using active thermography is a well-studied field, and many thermographic data analysis methods have been proposed to facilitate defect visibility enhancement. In this work, we introduce a deep learning method that is constrained by known heat transfer phenomena described by a series of governing equations, also known in the literature as the physics-informed neural network (PINN). The accurate reconstruction of background information based on thermal images facilitates the identification of subsurface defects and reduction in noises caused by an uneven background and heating. The authors illustrate the method’s feasibility through experimental results obtained after pulsed thermography (PT) on a carbon fiber-reinforced polymer (CFRP) specimen.